The invention relates generally to a positioning technique in which a target device's location is estimated on the basis of a sequence of observations on the target device's wireless communication environment. FIG. 1 schematically illustrates an example of such a positioning technique. A target device T communicates via base stations BS via a radio interface RI. In this example, the communication is assumed to be radio communication. The target device T. observes signal values at the radio interface RI. The observations O are applied to a probabilistic model PM that models the target device's wireless communication environment and produces a location estimate LE. As used herein, a target device is a device whose location is to be determined. The target device communicates via signals in a wireless environment, and signal values in the wireless environment are used for determining the target device's location. For example, the target device may be a data processing device communicating in a wireless local-area network (WLAN). The data processing device may be a general-purpose laptop or palmtop computer or a communication device, or it may be a dedicated test or measurement apparatus such as a hospital instrument connected to the WLAN. A location, as used herein, is a coordinate set of one to three coordinates. In some special cases, such as tunnels, a single coordinate may be sufficient but in most cases the location is expressed by a coordinate pair (x, y or angle/radius).
More particularly, the invention relates to a positioning technique that is based on a hidden Markov model. FIG. 2 schematically illustrates a hidden Markov model. The model consists of locations, transitions between the locations and observations made at the locations. In the example shown in FIG. 2, the target device moves along a path of which five locations qt−2 through qt+2 are shown. More formally, qt defines the location distribution at time t, so that P(qt=s) is the probability for the target device being at location s at time t. However, because a location distribution can easily be converted to a single location estimate, the shorthand notation “location q” will be used to refer to a location distribution q.
The locations along the target device's path can be called path points. The target device communicates via signals in a wireless environment, and signal values in the wireless environment are used for determining the target device's location.
A practical example of the target device is a data processing device communicating in a wireless local-area network (WLAN) or a cellular radio network. The data processing device may be a general-purpose laptop or palmtop computer or a communication device, or it may be a dedicated test or measurement apparatus such as a hospital instrument connected to the WLAN. A signal value, as used herein, is a measurable and location-dependent quantity of a fixed transmitter's signal. For example, signal strength and bit error rate/ratio are examples or measurable and location-dependent quantities.
The word ‘hidden’ in the hidden Markov model stems from the fact that we are primarily interested in the locations qt−2 through qt+2 but the locations are not directly observable. Instead we can make a series of observations ot−2 through ot+2 on the basis of the signal values but there is no simple relationship between the observations ot−2 . . . ot+2 and locations qt−2 . . . qt+2. (Note that the straight arrows through the locations qt−2 through qt+2 are not meant to imply that the target devices moves along a straight path or with a constant speed, or that the observations are made at equal intervals.)
A problem underlying the invention derives from the hidden Markov model: we cannot observe a variable that has a monotonous relationship with distance or location. Instead the positioning method is based on observations of signal values. It is possible for two or more locations to have near-identical sets of signal values, and a location estimate may be grossly inaccurate.